Parallel DEVS: a parallel, hierarchical, modular modeling formalism and its distributed simulator
Transactions of the Society for Computer Simulation International
DEVS formalism and methodology: unity of conception/diversity of application
WSC '93 Proceedings of the 25th conference on Winter simulation
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Automatica (Journal of IFAC)
GDEVS: A generalized discrete event specification for accurate modeling of dynamic systems
Transactions of the Society for Computer Simulation International
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WSC '80 Proceedings of the 12th conference on Winter simulation
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Analysis and simplifications of discrete event systems and jackson queueing networks
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Discrete Event Simulation of Hybrid Systems
SIAM Journal on Scientific Computing
Parallel discrete event simulation with application to continuous systems
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Continuous System Simulation
Numerical Recipes 3rd Edition: The Art of Scientific Computing
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An Introduction to Statistical Signal Processing
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LSSC'11 Proceedings of the 8th international conference on Large-Scale Scientific Computing
SAN lite-solver: a user-friendly software tool to solve SAN models
Proceedings of the 2012 Symposium on Theory of Modeling and Simulation - DEVS Integrative M&S Symposium
Proceedings of the Symposium on Theory of Modeling & Simulation - DEVS Integrative M&S Symposium
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We introduce an extension of the classic Discrete Event System Specification (DEVS) formalism that includes stochastic features. Based on the use of the probability spaces theory we define the stochastic DEVS (STDEVS) specification, which provides a formal framework for modeling and simulation of general non-deterministic discrete event systems. The main theoretical properties of the STDEVS framework are treated, including a new definition of legitimacy of models in the stochastic context and a proof of STDEVS closure under coupling. We also illustrate the new stochastic modeling capabilities introduced by STDEVS and their relation with those found in classic DEVS. Practical simulation examples are given involving performance analysis of computer systems and hybrid modeling of networked control systems, applications where the modeling of stochastic components is vital.